@InProceedings{muttil:76,
author = "N. Muttil and S. Y. Liong",
editor = "Don Phelps and Gerald Sehlke",
title = "Improving Runoff Forecasting by Input Variable
Selection in Genetic Programming",
publisher = "ASCE",
year = "2001",
booktitle = "World Water Congress 2001",
volume = "111",
pages = "76--76",
address = "Orlando, Florida, USA",
month = "20-24 " # may,
keywords = "genetic algorithms, genetic programming, Forecasting,
Runoff, Rainfall-runoff, relationships, Watersheds",
isbn13 = "978-0-7844-0569-7",
DOI = "doi:10.1061/40569(2001)76",
size = "7 pages",
abstract = "Determining the relationship between rainfall and
runoff for a watershed is one of the most important
problems faced by hydrologists and engineers. This
relationship is known to be highly complex with strong
correlation between the model parameters. In any model
development process, the selection of appropriate model
inputs is extremely important. Many authors in the past
have attempted to address the issue of selecting the
most relevant parameters of a given data set based on
sensitivity analysis, yet the effect of interaction of
variables is not clearly expatiated. In this study, we
use the Group Method of Data Handling (GMDH) technique
for selecting the significant variables to be used as
input to Genetic Programming, which leads to improved
runoff forecasting. The main advantage of GMDH
technique is that it considers the interaction amongst
the variables while selecting the ones that are
significant.",
notes = "number = 40569",
}